{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 茅台历史净值序列导出\n",
    "\n",
    "$$\n",
    "a=b+c\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 调用聚宽的python SDK API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from jqdatasdk import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "auth success \n"
     ]
    }
   ],
   "source": [
    "# auth('ID','Password')\n",
    "\n",
    "auth('13811550483','security421S')\n",
    "pd.set_option('display.max_rows', 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>display_name</th>\n",
       "      <th>name</th>\n",
       "      <th>start_date</th>\n",
       "      <th>end_date</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>600519.XSHG</th>\n",
       "      <td>贵州茅台</td>\n",
       "      <td>GZMT</td>\n",
       "      <td>2001-08-27</td>\n",
       "      <td>2200-01-01</td>\n",
       "      <td>stock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            display_name  name start_date   end_date   type\n",
       "600519.XSHG         贵州茅台  GZMT 2001-08-27 2200-01-01  stock"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = get_all_securities()\n",
    "GZMT = df[df['name']=='GZMT']\n",
    "GZMT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_price = get_price(GZMT.index[0], start_date='2018-01-01', end_date='2020-02-26', frequency='daily', fields=['open', 'close', 'low', 'high'], skip_paused=False, fq='pre')\n",
    "stock_price.to_csv('GZMTprice.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\zhenchen\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\pandas\\plotting\\_converter.py:129: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n",
      "\n",
      "To register the converters:\n",
      "\t>>> from pandas.plotting import register_matplotlib_converters\n",
      "\t>>> register_matplotlib_converters()\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.plot(stock_price.index,stock_price)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
